Machine learning in forensic toxicology: Concepts, applications and challenges in bioanalysis, ADME, and toxicodynamics

Abstract

Forensic toxicology focuses on the detection, quantification, and interpretation of medicinal and recreational drugs, other chemicals or poisons, and their metabolites in biological matrices. Chromatography, combined with mass spectrometry (MS), is the most widely used analytical technique. However, forensic toxicology faces increasing analytical challenges due to a continuously changing drug landscape. In particular, the emergence of new psychoactive substances (NPS) has driven the development of more complex analytical methods (e.g., high-resolution mass spectrometry), novel markers (e.g., metabolomics), or innovative screening approaches (e.g., activity-based), which collectively generate vast amounts of data. These challenges include rapid market dynamics with the constant emergence of new chemical scaffolds and modifications, complex fragmentation and metabolic behavior, and limited or delayed access to reference materials- These developments are not limited to NPS alone. Consequently, machine learning (ML) algorithms have increasingly found their way into forensic toxicology. This review discusses various applications of ML methods related to bioanalysis, metabolomics, and toxicodynamics in the context of forensic toxicology. Currently, a major limitation is the compilation of sufficiently large and suitable datasets, which is often constrained by limited availability of real case data, inhomogeneous analytical data, in vivo study designs with small group size (< 10 animals per group), or a low number of included substances. Ultimately, the quality of an ML model relies not only on data quality but also on a thorough understanding of analytical chemistry, biochemistry, pharmacology, medical case history, and ML design, highlighting the importance of interdisciplinary collaboration in these studies.